📌HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this paper, Yuval Alaluf et al. propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space.
https://bit.ly/3oLRhUE
In this paper, Yuval Alaluf et al. propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space.
https://bit.ly/3oLRhUE
⚡️Hello, friends! Let’s start this week in a company of Tom Davenport, a world-renowned thought leader in all things IT.
Dr. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.
He is also the author and co-author of 20 books and more than 200 articles. He is dedicated to helping organizations transform their management practices in digital business domains, such as artificial intelligence, analytics, and enterprise systems.
Over the course of 40 years, he has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. He earned his Ph.D from Harvard University and has taught at the Harvard Business School, the University of Chicago, and the University of Texas at Austin.
https://bit.ly/30k3DKm
Dr. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.
He is also the author and co-author of 20 books and more than 200 articles. He is dedicated to helping organizations transform their management practices in digital business domains, such as artificial intelligence, analytics, and enterprise systems.
Over the course of 40 years, he has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. He earned his Ph.D from Harvard University and has taught at the Harvard Business School, the University of Chicago, and the University of Texas at Austin.
https://bit.ly/30k3DKm
💡XGBoost vs LightGBM: How Are They Different
XGBoost and LightGBM are two of the most popular algorithms that are based on Gradient Boosted Machines. Let's find out how different they are, with pros and cons for each.
https://bit.ly/31WurRx
XGBoost and LightGBM are two of the most popular algorithms that are based on Gradient Boosted Machines. Let's find out how different they are, with pros and cons for each.
https://bit.ly/31WurRx
neptune.ai
XGBoost vs LightGBM: How Are They Different
Gradient Boosted Machines and their variants offered by multiple communities have gained a lot of traction in recent years. This has been primarily due to the improvement in performance offered by decision trees as compared to other machine learning algorithms…
📚EditGAN: High-Precision Semantic Image Editing
In this paper, Huan Ling et al. present EditGAN, the first GAN-driven image editing framework that outperforms previous editing methods on standard editing benchmark tasks.
https://bit.ly/3s4OI2g
In this paper, Huan Ling et al. present EditGAN, the first GAN-driven image editing framework that outperforms previous editing methods on standard editing benchmark tasks.
https://bit.ly/3s4OI2g
💡Text-based Causal Inference
In this tutorial, you'll learn about a new method of analyzing voter fraud disinformation by estimating causal effect with text as treatment and confounder.
https://bit.ly/3EZm6ee
In this tutorial, you'll learn about a new method of analyzing voter fraud disinformation by estimating causal effect with text as treatment and confounder.
https://bit.ly/3EZm6ee
Medium
Text-based Causal Inference
Tutorial on analyzing voter fraud disinformation by estimating causal effect with text as treatment and confounder
📚Adobe Research at ICCV 2021
This page features a set of 45 papers, including 5 oral papers, 34 posters, and 6 workshop papers presented by Adobe at IEEE Computer Society International Conference on Computer Vision.
https://adobe.ly/3F0UW6V
This page features a set of 45 papers, including 5 oral papers, 34 posters, and 6 workshop papers presented by Adobe at IEEE Computer Society International Conference on Computer Vision.
https://adobe.ly/3F0UW6V
Adobe Research
Adobe Research at ICCV 2021
Adobe actively participates in the IEEE Computer Society International Conference on Computer Vision (ICCV) each year. At this year's conference, taking place from October 11-17, Adobe is presenting new work in the area of computer vision. Check out the full…
⚡️Hello everyone, it's Data Phoenix speaking!
We hope that your week is going well so far. Our team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3IXSXm3
We hope that your week is going well so far. Our team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3IXSXm3
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
📌Get Started: DCGAN for Fashion-MNIST
This tutorial will guide you through the implementation of a Deep Convolutional GAN (DCGAN) with TensorFlow 2 / Keras. It's based on a classic GAN paper.
https://bit.ly/33rQQXo
This tutorial will guide you through the implementation of a Deep Convolutional GAN (DCGAN) with TensorFlow 2 / Keras. It's based on a classic GAN paper.
https://bit.ly/33rQQXo
PyImageSearch
Get Started: DCGAN for Fashion-MNIST - PyImageSearch
Get started learning GANs by implementing a DCGAN with TensorFlow 2 / Keras to generate Fashion-MNIST like gray-scale images.
💥Hello everyone! Data Phoenix Speaking!
We are ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3p3TPOc
We are ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3p3TPOc
Data Phoenix
Data Phoenix Digest - ISSUE 36
Data Phoenix team looking for speakers, MLCommons presents free and open-source datasets for speech recognition, accelerating inference up to 6x, document understanding transformer without OCR, GradInit, NÜWA, videos, jobs, and more ...
⚡️Hello everyone!
We hope that your weekend is going great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Senior Data Scientist - Finance Data - Coinbase (Remote - USA)
https://bit.ly/3F9CZTK
2) Senior Software Engineer - Data Pipeline - GitHub (Remote - USA)
https://bit.ly/3mhpDNL
3) Machine Learning Engineer - Cloudflare (Remote - USA)
https://bit.ly/3shnqpd
4) Principal Data Scientist - Intercom (Remote in Ireland or the UK)
https://bit.ly/32k5dg8
5) Cloud Data Engineer - Rackspace (Remote - USA)
https://bit.ly/3E9zCdY
📌Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
We hope that your weekend is going great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Senior Data Scientist - Finance Data - Coinbase (Remote - USA)
https://bit.ly/3F9CZTK
2) Senior Software Engineer - Data Pipeline - GitHub (Remote - USA)
https://bit.ly/3mhpDNL
3) Machine Learning Engineer - Cloudflare (Remote - USA)
https://bit.ly/3shnqpd
4) Principal Data Scientist - Intercom (Remote in Ireland or the UK)
https://bit.ly/32k5dg8
5) Cloud Data Engineer - Rackspace (Remote - USA)
https://bit.ly/3E9zCdY
📌Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
📌Learning Rates for Deep Learning Models
How can you make your deep learning models as effective as possible? In this article, you'll learn about the effects of a learning rate on the convergence and performance of DL models.
https://bit.ly/3e3Q1WV
How can you make your deep learning models as effective as possible? In this article, you'll learn about the effects of a learning rate on the convergence and performance of DL models.
https://bit.ly/3e3Q1WV
Medium
Learning Rates for Deep Learning Models
How to make good models great through optimization
🔥Hello friends!
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3yMTe6L
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3yMTe6L
📚NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
NÜWA is a unified multimodal pre-trained model that can generate new or manipulate existing visual data (i.e., images and videos) for various visual synthesis tasks.
https://bit.ly/33Id9s9
NÜWA is a unified multimodal pre-trained model that can generate new or manipulate existing visual data (i.e., images and videos) for various visual synthesis tasks.
https://bit.ly/33Id9s9
⚡️Happy Monday, friends! Data Phoenix team wishes you an amazing week! Today, we want to present to you Tamara McCleary
Tamara is the founder and CEO of a global social media marketing agency, Thulium. Currently, she’s a full-time graduate student at Harvard. As a research scientist at the Harvard Kennedy School, she studies science, technology, ethics, global economics, and public policy.
Tamara is interested in algorithmic governance, as well as the social, moral, ethical, and spiritual implications of genetic engineering. She prefers exploring various existential questions, and the future of religion in the world where life extension technologies, synthetic biology, artificial general intelligence, and our reliance on machines create a transhumanist and post-humanist existence.
She is also an advisor and crew member for the Proudly Human Off-World Projects, which simulates resource-constrained environments in search of solutions for those living off-world.
https://bit.ly/3skl5db
Tamara is the founder and CEO of a global social media marketing agency, Thulium. Currently, she’s a full-time graduate student at Harvard. As a research scientist at the Harvard Kennedy School, she studies science, technology, ethics, global economics, and public policy.
Tamara is interested in algorithmic governance, as well as the social, moral, ethical, and spiritual implications of genetic engineering. She prefers exploring various existential questions, and the future of religion in the world where life extension technologies, synthetic biology, artificial general intelligence, and our reliance on machines create a transhumanist and post-humanist existence.
She is also an advisor and crew member for the Proudly Human Off-World Projects, which simulates resource-constrained environments in search of solutions for those living off-world.
https://bit.ly/3skl5db
💡Visualizing the vanishing gradient problem
In this tutorial, you'll learn why the vanishing gradient problem exists, including its 101, do's and don'ts, and all about configurations of neural networks susceptible to vanishing gradient.
https://bit.ly/3yHl6sQ
In this tutorial, you'll learn why the vanishing gradient problem exists, including its 101, do's and don'ts, and all about configurations of neural networks susceptible to vanishing gradient.
https://bit.ly/3yHl6sQ
Machine Learning Mastery
Visualizing the vanishing gradient problem - Machine Learning Mastery
Deep learning was a recent invention. Partially, it is due to improved computation power that allows us to use more layers of perceptrons in a neural network. But at the same time, we can train a deep network only after we know how to work around the vanishing…
📚GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
GradInit is an automated and architecture agnostic method for initializing neural networks. It improves the stability of the original Transformer architecture for machine translation.
https://bit.ly/3pbIRX0
GradInit is an automated and architecture agnostic method for initializing neural networks. It improves the stability of the original Transformer architecture for machine translation.
https://bit.ly/3pbIRX0
📌Meta-Learning for Keyphrase Extraction
This article explores how to build a key phrase extractor that performs on in-domain data and in zero-shot scenarios where keyphrases need to be extracted from data with different distributions.
https://bit.ly/32sN3J6
This article explores how to build a key phrase extractor that performs on in-domain data and in zero-shot scenarios where keyphrases need to be extracted from data with different distributions.
https://bit.ly/32sN3J6
Medium
Meta-Learning for Keyphrase Extraction
A strategy to boost zero-shot predictions
📚End-to-End Referring Video Object Segmentation with Multimodal Transformers
This paper presents Multimodal Tracking Transformer (MTTR) that models the RVOS task as a sequence prediction problem. It simplifies the RVOS pipeline compared to existing methods.
https://bit.ly/3H85LVq
This paper presents Multimodal Tracking Transformer (MTTR) that models the RVOS task as a sequence prediction problem. It simplifies the RVOS pipeline compared to existing methods.
https://bit.ly/3H85LVq
⚡️Hello everyone!
We hope that your week is going well so far. Data Phoenix team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3ElROBu
We hope that your week is going well so far. Data Phoenix team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3ElROBu
Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
💡Accelerating Inference Up to 6x Faster in PyTorch with Torch-TensorRT
Torch-TensorRT is the new integration of PyTorch with NVIDIA TensorRT, which accelerates the inference with one line of code. Learn how you can start using it today!
https://bit.ly/3eqGY2g
Torch-TensorRT is the new integration of PyTorch with NVIDIA TensorRT, which accelerates the inference with one line of code. Learn how you can start using it today!
https://bit.ly/3eqGY2g
NVIDIA Developer Blog
Accelerating Inference Up to 6x Faster in PyTorch with Torch-TensorRT | NVIDIA Developer Blog
Torch-TensorRT is a PyTorch integration for TensorRT inference optimizations on NVIDIA GPUs. With just one line of code, it speeds up performance up to 6x.
🎄This is the most magical period of the year!
So, even if you are a hardcore science guy, let’s give each other a chance to believe in something special! Data Phoenix team wishes you health, peace, love, and joy this holiday season and throughout 2022. We appreciate every single one of you and we hope that in 2022 we would expand our family of data enthusiasts even more.
❄️Merry Christmas and Happy New Year!
So, even if you are a hardcore science guy, let’s give each other a chance to believe in something special! Data Phoenix team wishes you health, peace, love, and joy this holiday season and throughout 2022. We appreciate every single one of you and we hope that in 2022 we would expand our family of data enthusiasts even more.
❄️Merry Christmas and Happy New Year!